11 min read
You sit down on the couch, open Netflix, and start browsing. Within thirty seconds, the app knows you’re awake, what device you’re using, your approximate location, what time you typically watch, and what genres you’ve been gravitating toward lately. Before you even press play, Netflix has logged several data points about your session.
Most people have a vague sense that streaming services collect data. The recommendation algorithm has to learn your preferences somehow, right? But the reality of what these platforms track, store, and monetize goes far beyond what most subscribers expect. In our guide to which streaming services are worth paying for, we compared content and pricing – but cost is only half the equation. After spending weeks reading privacy policies, data practices reports, and research from digital rights organizations, I have a much clearer picture of the trade-off we’re making when we hit “play.” It’s not as comfortable as the couch.
What Streaming Services Actually Collect
Every major streaming platform collects data, but the scope varies. Let me break down the major categories of information these services gather, based on their own privacy policies (which, by the way, average over 4,000 words each and are written in language designed to be technically transparent while remaining practically opaque).
Account and Identity Data
This is the obvious stuff: your name, email address, payment information, billing address, phone number, and date of birth. If you signed up through a third party (like Apple ID or Google), they also receive whatever data that platform shares during authentication. At minimum, make sure each streaming account uses a unique, strong password – a password generator is the easiest way to do this. Most services also store profile information for multiple users under one account, including separate viewing histories and preferences for each profile.
Viewing Behavior
This is where it gets granular. Services don’t just track what you watch. They track when you watch, how long you watch, where you pause, where you rewind, where you fast-forward, and where you stop. They know if you watched the credits or skipped them. They know if you binged eight episodes in a row or watched one per week. They record what you searched for, what you browsed but didn’t select, and what you added to your list but never actually watched.
Netflix’s recommendation system reportedly uses over 1,300 recommendation clusters to categorize viewing behavior. That’s not 1,300 genres; that’s 1,300 micro-categories of viewing patterns that the algorithm uses to predict what you’ll watch next. The system knows the difference between “watches dark crime dramas late at night on weekdays” and “watches dark crime dramas on weekend afternoons.” These are different behavioral profiles that get different recommendations.
Device and Technical Data
Your device type, operating system, browser version, IP address, network information, screen resolution, audio setup, and bandwidth. Services use this partly for technical optimization (delivering the right video quality for your connection) and partly for identification. Your combination of device characteristics creates a “fingerprint” that can identify you even without explicit login data.
Location data deserves special attention. Even without GPS access, your IP address reveals your approximate geographic location. Streaming services use this for content licensing (some content is only available in certain regions), but they also use it for behavioral analysis. The Electronic Frontier Foundation has noted that location data combined with viewing patterns can reveal surprisingly personal information about subscribers, including daily routines, travel patterns, and even relationship status changes.
Interaction Data
Every click, scroll, hover, and tap is logged. How long you spend looking at a title card before moving on. Whether you read the description. Which row of recommendations you engaged with. Whether you responded to a push notification. This interaction data feeds directly into the recommendation algorithm but also into A/B testing systems that optimize the user interface for engagement (read: keeping you watching longer).
The Ad-Supported Tier Privacy Gap
If you’re on an ad-supported plan, the data collection situation is meaningfully different. Ad-supported tiers introduce advertising partners into the data equation, and those partners have their own tracking and data collection practices.
When Netflix, Disney+, or Max show you an ad, they’re sharing data with advertising networks to enable targeted advertising. This typically includes demographic information, viewing preferences, device data, and behavioral patterns. The streaming service might not share your name directly, but they share enough data points that advertisers can build detailed profiles.
Netflix’s ad-supported tier uses Microsoft’s advertising platform. Disney+ works with multiple ad tech partners. Each of these partnerships involves data sharing agreements that extend your personal information to companies you’ve never heard of and didn’t knowingly consent to share with. The privacy policy technically covers this, but buried in paragraph 47 of a document nobody reads.
The practical difference is significant. A premium subscriber’s data primarily stays within the streaming platform’s ecosystem. An ad-tier subscriber’s data flows to third-party advertising networks, data brokers, and measurement companies. According to research from the Mozilla Foundation, ad-supported streaming tiers share data with an average of 10-15 third-party companies per session.
How Recommendation Algorithms Use Your Data
The recommendation algorithm is the most visible use of your data, and it’s worth understanding how it actually works because it affects what content you’re exposed to in ways that aren’t always obvious.
Modern recommendation systems use collaborative filtering (finding users with similar viewing patterns and recommending what they watched), content-based filtering (analyzing the attributes of content you’ve enjoyed and finding similar titles), and increasingly, deep learning models that identify patterns humans might not recognize.
The result is a feedback loop. The algorithm shows you content similar to what you’ve watched before. You watch it (because it’s what’s being shown to you). The algorithm then reinforces those preferences. Over time, your recommendation feed narrows, creating what researchers call a “filter bubble” where you’re increasingly served the same types of content. This is the same mechanism at work in news algorithms that decide what stories you see – platforms optimizing for engagement rather than breadth.
This isn’t inherently sinister, but it does have effects worth thinking about. The algorithm optimizes for engagement, not satisfaction. There’s a difference between “content that keeps you watching” and “content you’re glad you watched.” The autoplay feature, the “skip intro” button, the countdown to the next episode – these are all designed to reduce friction between episodes, which serves the platform’s engagement metrics even if it doesn’t serve your actual wellbeing.
What They Do With Data Beyond Recommendations
Recommendations are the consumer-facing use of your data. Behind the scenes, streaming services use viewing data for several other purposes that directly affect business decisions.
Content investment decisions. When Netflix decides whether to renew a show, they’re not just looking at how many people watched it. They’re analyzing completion rates, replay rates, whether it drove new subscriptions, whether viewers who watched it had lower churn rates, and dozens of other behavioral metrics. A show that lots of people start but few finish gets treated very differently from a show with a small but highly engaged audience.
Pricing optimization. Your viewing behavior and engagement patterns feed into models that help determine pricing strategy. Services can identify which subscribers are most likely to cancel after a price increase and target retention offers accordingly. They can also identify which content is most effective at preventing churn, which influences what gets renewed or canceled.
Marketing and acquisition. Aggregate viewing data helps streaming services target potential subscribers. If the data shows that people who watch true crime documentaries have high retention rates, marketing campaigns can be specifically targeted at true crime fans on social media.
Licensing negotiations. Viewing data gives platforms enormous leverage in content licensing negotiations. If Netflix knows exactly how many people watched a particular licensed show and how it affected subscriber retention, they can make precise offers during renewal negotiations. This data asymmetry favors the platforms over content creators.
The Legal Framework (Or Lack Thereof)
In the European Union, the General Data Protection Regulation (GDPR) gives users meaningful rights over their personal data, including the right to access, correct, and delete it. EU subscribers can request a copy of all data a streaming service holds about them, and the results are often eye-opening.
In the United States, there’s no comprehensive federal data privacy law. The Video Privacy Protection Act (VPPA), passed in 1988 after a reporter obtained Supreme Court nominee Robert Bork’s video rental history, technically applies to streaming services. But the VPPA was written for VHS rental stores and hasn’t been meaningfully updated for the streaming era. It prohibits disclosure of viewing records without consent but doesn’t restrict what companies can collect or how they use data internally.
California’s CCPA and its successor CPRA provide some protections for California residents, including the right to know what data is collected, the right to delete it, and the right to opt out of data sales. A handful of other states have passed similar laws. But for most Americans, there are minimal legal restrictions on what streaming services can collect and retain.
This means the privacy protections you receive depend largely on where you live. A subscriber in Berlin has significantly more control over their data than a subscriber in Dallas, even though they’re using the exact same service.
What a Data Request Actually Reveals
I submitted data access requests to three streaming services I use: Netflix, Disney+, and Max. Under GDPR (or CCPA if you’re in California), you have the right to request a copy of your data. Here’s a summary of what came back.
Netflix’s data export was the most comprehensive. It included my complete viewing history (every title, every session, including partial views), my search history, my ratings, my account settings and changes over time, device login history with IP addresses, billing history, and the “taste profile” the algorithm had built for me. The taste profile was the most interesting part: it listed hundreds of weighted tags describing my apparent preferences, some of which were surprisingly accurate and some of which were hilariously wrong.
The viewing history was the most revealing. It showed not just what I watched but exactly when I watched it. You could reconstruct my daily routine from the data: when I wake up, when I go to bed, when I’m home on weekends, and when I traveled (based on IP address changes). That level of temporal detail feels more invasive than I expected, even though I knew intellectually that it was being collected.
Disney+ and Max provided less detailed exports, but the core data was similar: viewing history, account information, device records, and payment data. Disney’s export included a notable amount of data about how I interacted with their app interface – which menus I opened, which categories I browsed, and how long I spent on the home screen versus actively watching. Max’s export was the smallest of the three but still included several years of detailed viewing timestamps.
The most sobering realization from reviewing these data exports wasn’t any single data point. It was the aggregate picture. Combined, three streaming services had a detailed record of hundreds of hours of my free time, organized by date, time, and location. They knew my preferences, my habits, my schedule, and my taste profile. If someone wanted to build a behavioral profile of me, they wouldn’t need surveillance cameras. They’d just need my streaming data.
Practical Steps to Protect Your Privacy
Complete privacy from streaming services is essentially impossible if you want to use them. But there are meaningful steps you can take to reduce your data footprint and limit how your information gets shared.
Pay for Ad-Free Tiers
The single biggest thing you can do for your streaming privacy is avoid ad-supported plans. Yes, they’re cheaper. But the privacy cost is substantial. Ad-supported tiers introduce third-party tracking that significantly expands who has access to your viewing data. If you can afford the difference, the ad-free tier is worth it from a privacy perspective alone, setting aside the better viewing experience.
Use Separate Profiles Thoughtfully
If you share an account with family members, use separate profiles. This isn’t just about recommendation quality; it’s about data segmentation. Each profile builds its own behavioral profile. If your kids are watching on your profile, their viewing patterns get mixed into your data, creating a combined profile that’s shared with advertisers and partners.
Review and Clear Your Viewing History
Most streaming services allow you to view and delete your viewing history. Netflix lets you remove individual titles or clear everything. This doesn’t delete the data from their servers entirely (they retain it for business purposes), but it does affect what the recommendation algorithm uses going forward and what shows up in your data exports.
Limit Device Permissions
Streaming apps on mobile devices often request permissions they don’t strictly need: location access, microphone access (ostensibly for voice search), contact access, and notification permissions. Review and revoke any permissions that aren’t essential for basic playback. On iOS, you can check this under Settings and then Privacy. On Android, go to Settings, then Apps, then Permissions.
Use a VPN Selectively
A VPN masks your IP address, which limits the location data streaming services can collect. However, most streaming services actively block VPN traffic, and using a VPN may violate their terms of service. This is a judgment call based on your personal privacy priorities.
Submit Data Access Requests
Even if you’re not in the EU or California, it’s worth submitting a data access request to your streaming services. Many companies will comply regardless of your location, and seeing the actual data they hold about you is a powerful motivator for adjusting your privacy habits. Look for “Privacy” or “Data Request” options in your account settings, or search the service’s help center for GDPR or CCPA data requests.
The Privacy Cost of Convenience
After spending weeks with this research, my overall takeaway is this: streaming services offer a genuine trade-off, and most people make that trade without understanding what they’re giving up.
The data collection isn’t accidental or incidental. It’s a core part of the business model. Your viewing behavior is a product that streaming services use for internal decision-making, sell to advertisers (on ad tiers), and leverage in content negotiations. The service you pay for with money, you also pay for with information.
I’m not suggesting people stop using streaming services. I use them daily, and I’ll continue to. But I’ve made some changes based on what I’ve learned. I’ve switched to ad-free tiers everywhere. I’ve revoked unnecessary app permissions. I periodically clear my viewing history. And I’ve become much more intentional about what I search for and browse, knowing that every interaction generates data points.
The streaming industry is still young, and the privacy norms are still being established. The choices subscribers make now, the features they demand, the tiers they choose, and the laws they support will shape how these platforms handle personal data for decades to come. Understanding what’s actually happening with your data is the first step toward making informed decisions about which trade-offs you’re comfortable with.
Request your data. Read what comes back. Then decide for yourself what your viewing habits are worth.
